We consider a parallel, rule-based approach for learning and recognition of
pattern and objects in scenes. Classification rules for pattern fragments
are learned with objects presented in isolation and are based on unary
features of pattern parts and binary features of part relations. These rules
are then applied to scenes composed of multiple objects. We
present an approach that solves, at the same time, evidence combination and
consistency analysis of multiple rule instantiations. Finally, we introduce
an extension of our approach to the learning of dynamic patterns.